Space Intelligence Firm InSpatio Launches China's First Embodied Simulation Data Center, Claiming 1 Million Hours Monthly Capacity at One-Tenth Cost
InSpatio introduces an industrial pipeline for generating embodied AI training data, aiming to overcome scarcity and high costs associated with physical collection.
On October 30, space intelligence company InSpatio launched China's first embodied simulation data collection center in Hangzhou. According to InSpatio, the facility is projected to produce 1 million hours of simulation training data per month, reducing the comprehensive cost per data unit to approximately one-tenth of traditional real-machine teleoperation methods. This initiative aims to address the persistent shortage of high-quality training data in the field of embodied intelligence.
Currently, data acquisition for embodied AI relies on two main paths: physical world collection via real-machine teleoperation and first-person view (EGO) wearable devices, and digital world generation through 3D modeling and physics simulation. However, both face efficiency bottlenecks. Physical collection is limited by time, site access, and environmental adjustment flexibility, often yielding less than half an hour of effective trajectory per operator-hour. Traditional simulation modeling depends on manual scene construction, taking days or weeks, and struggles to maintain geometric and physical consistency, leading to the 'Real2Sim Gap' during transfer to reality.
InSpatio's proposed solution is to build an 'Embodied Token Factory.' Using its proprietary Topos spatial intelligence base, the system converts real-world spaces into interactive digital scenes through three automated industrial lines. Upstream, portable scanning or native 3D generation captures over 100 square meters of space in 15 minutes. The system then performs object decomposition, blind-spot completion, and physical calibration to transform these scans into runnable digital masters within hours.
Midstream, once scenes are digitized, the system can launch multiple parallel simulation channels in the cloud. The underlying physics engine calculates mechanical results in real-time, filtering out failures to batch-generate embodied tokens containing depth, pose, contact force, and action semantics. This shifts data production volume from reliance on manual on-site setup to dependence on digital scene reusability and cloud computing scale.